A novel approach for the characterisation of proteoglycans and biosynthetic enzymes in a snail model

Proteoglycans encompass a heterogeneous group of glycoconjugates where proteins are substituted with linear, highly negatively charged glycosaminoglycan chains. Sulphated glycosaminoglycans are ubiquitous to the animal kingdom of the Eukarya domain. Information on the distribution and characterisation of proteoglycans in invertebrate tissues is limited and restricted to a few species. By the use of multidimensional protein identification technology and immunohistochemistry, this study shows for the first time the presence and tissue localisation of different proteoglycans, such as perlecan, aggrecan, and heparan sulphate proteoglycan, amongst others, in organs of the gastropoda Achatina fulica. Through a proteomic analysis of Golgi proteins and immunohistochemistry of tissue sections, we detected the machinery involved in glycosaminoglycan biosynthesis, related to polymer formation (polymerases), as well as secondary modifications (sulphation and uronic acid epimerization). Therefore, this work not only identifies both the proteoglycan core proteins and glycosaminoglycan biosynthetic enzymes in invertebrates but also provides a novel method for the study of glycosaminoglycan and proteoglycan evolution. (C) 2011 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)NIHUniversidade Federal de São Paulo, Dept Bioquim, BR-04044020 São Paulo, BrazilUniv Texas El Paso, Dept Biol Sci, Border Biomed Res Ctr, El Paso, TX 79912 USAUniversidade Federal de São Paulo, Dept Bioquim, BR-04044020 São Paulo, BrazilNIH: 2G12RR008124-16A1NIH: 2G12RR008124-16A1S1Web of Scienc

Low molecular weight heparins: Structural differentiation by spectroscopic and multivariate approaches

Various branded low molecular weight heparins (LMWHs) have been used for the treatment and prevention of thrombotic for over 20 years. With the introduction of generic LMWHs and the recent events involving heparin contamination, a great deal of effort is being expended in investigating ways of monitoring and regulating this class of complex drugs. in this paper, we present the characterization of different forms of LMWHs, as well as the comparison of 5 enoxaparin copies from different manufactures. the data suggests that, while some of these drugs are structurally comparable, specific analytical methods as well as biological and pharmacological tests may be used to address their similarity, quality and potential interchangeability. the proposed approach may also be useful in comparing biosimilar and branded LMWHs. (C) 2011 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Universidade Federal de São Paulo, Dept Bioquim, BR-04044020 São Paulo, SP, BrazilUniv Liverpool, Sch Biol Sci, Liverpool L69 7ZB, Merseyside, EnglandLoyola Univ, Med Ctr, Dept Pathol, Maywood, IL 60153 USAUniv Fed Parana, Lab Quim Carboidratos, Dept Bioquim & Biol Mol, BR-81531980 Curitiba, Parana, BrazilUniversidade Federal de São Paulo, Dept Bioquim, BR-04044020 São Paulo, SP, BrazilWeb of Scienc

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

SARS-CoV-2 uses CD4 to infect T helper lymphocytes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

American oil palm from Brazil: genetic diversity, population structure, and core collection.

The American oil palm [Elaeis oleifera (Knuth) Cortés] has pronounced importance in oil palm breeding programs. Here, a germplasm bank (GB) of E. oleifera plants collected in the Amazon rainforest in Brazil was submitted to single nucleotide polymorphism (SNP) marker identification, selection, and use, aiming to characterize genetic diversity and population structure and to design a core collection (CC). Five hundred and fifty-three plants from 206 subsamples, collected at 19 localities spread throughout six geographic regions, were submitted to genotyping-by-sequencing analysis. A set of 1,827 high-quality SNP markers was then selected and used to run the genetic diversity and population structure analysis. The genetic diversity found is of moderate degree, and probably only a small portion of the species diversity is represented in the collection. The possible reason for that is the collecting strategy used, which collected subsamples only around the most prominent watercourses in the region. The average degree of genetic differentiation among subsamples is very high, indicating the presence of high interpopulation differentiation. The collection showed a low level of endogamy. The low average gene flow found indicates that genetic isolation caused by drift is occurring, and there is a need to review the conservation strategy. A set of 245 SNPs distributed throughout all 16 chromosomes was used to design CC based on maximizing the strategy of diversity. The optimal adjustment of the validated parameters, maintained while taking fewest subsamples, led to the choice of a model containing 20% of the entire collection as the ideal to form the CC

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to &lt;90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], &gt;300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of &lt;15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P&lt;0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P&lt;0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost